| Paper ID | HLT-11.4 |
| Paper Title |
HANDLING CLASS IMBALANCE IN LOW-RESOURCE DIALOGUE SYSTEMS BY COMBINING FEW-SHOT CLASSIFICATION AND INTERPOLATION |
| Authors |
Vishal Sunder, Eric Fosler-Lussier, The Ohio State University, United States |
| Session | HLT-11: Language Understanding 3: Speech Understanding - General Topics |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-DIAL] Discourse and Dialog |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels. We present a new end-to-end pairwise learning framework that is designed specifically to tackle this phenomenon by inducing a few-shot classification capability in the utterance representations and augmenting data through an interpolation of utterance representations. Our approach is a general purpose training methodology, agnostic to the neural architecture used for encoding utterances. We show significant improvements in macro-F1 score over standard cross-entropy training for three different neural architectures, demonstrating improvements on a Virtual Patient dialogue dataset as well as a low-resourced emulation of the Switchboard dialogue act classification dataset. |
